Businesses worldwide recognize the commercial value of their data and seek reliable, cost-effective ways to protect the information stored on their computer networks while minimizing impact on productivity. Protecting information is often part of a routine process that is performed within an organization. A company might back up critical computing systems such as databases, file servers, web servers, and so on as part of a daily, weekly, or monthly maintenance schedule. The company may similarly protect computing systems used by each of its employees, such as those used by an accounting department, marketing department, engineering department, and so forth.
Given the rapidly expanding volume of data under management, companies also continue to seek innovative techniques for managing data growth, in addition to protecting data. For instance, companies often implement migration techniques for moving data to lower cost storage over time and data reduction techniques for reducing redundant data, pruning lower priority data, etc. Enterprises also increasingly view their stored data as a valuable asset. Along these lines, customers are looking for solutions that not only protect and manage, but also leverage their data. For instance, solutions providing data analysis capabilities, information management, improved data presentation and access features, and the like, are in increasing demand.
The expanding volume of data makes organizing the data more difficult. The data can be classified in various ways and stored in different locations according to the classification. For example, the data can be ordered by date of creation or date of last access, and the data having these dates before a threshold date can be migrated from primary storage devices to secondary storage devices. While such data migration frees up space on the primary storage devices, it adds to the workload of the server that is responsible for migrating data and the amount and complexity of the data on the secondary storage devices. In addition, the condition that triggers such data migration can change quickly over time, and applications that encode the trigger logic would need to change accordingly. As the application logic layer provides user interfaces and often involves complex control logic, updating application logic layer can be tedious and time-consuming, adversely affect user experience.
The expanding volume of data also makes finding the desired information more difficult. While users today often expect instantaneous, real-time response from computer systems, it can take a long time for a server to process a query or generate a report against such expanding volume of data, especially when the server already has a heavy workload.